Overview

Dataset statistics

Number of variables35
Number of observations425119
Missing cells1830655
Missing cells (%)12.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory113.5 MiB
Average record size in memory280.0 B

Variable types

Numeric19
DateTime1
Text8
Categorical7

Alerts

Ball Rebowled is highly imbalanced (75.7%)Imbalance
Extra Type is highly imbalanced (87.5%)Imbalance
Wicket is highly imbalanced (69.0%)Imbalance
Valid Ball is highly imbalanced (75.7%)Imbalance
Method has 401460 (94.4%) missing valuesMissing
Player Out has 401460 (94.4%) missing valuesMissing
Runs to Get has 224815 (52.9%) missing valuesMissing
Player Out Runs has 401460 (94.4%) missing valuesMissing
Player Out Balls Faced has 401460 (94.4%) missing valuesMissing
Unnamed: 0 is uniformly distributedUniform
Unnamed: 0 has unique valuesUnique
Batter Runs has 184757 (43.5%) zerosZeros
Extra Runs has 399765 (94.0%) zerosZeros
Runs From Ball has 160394 (37.7%) zerosZeros
Innings Runs has 4580 (1.1%) zerosZeros
Innings Wickets has 77021 (18.1%) zerosZeros
Total Batter Runs has 55592 (13.1%) zerosZeros
Total Non Striker Runs has 50366 (11.8%) zerosZeros
Batter Balls Faced has 25027 (5.9%) zerosZeros
Non Striker Balls Faced has 33793 (7.9%) zerosZeros
Bowler Runs Conceded has 168659 (39.7%) zerosZeros

Reproduction

Analysis started2024-03-10 17:38:47.128894
Analysis finished2024-03-10 17:40:52.142683
Duration2 minutes and 5.01 seconds
Software versionydata-profiling vv4.6.5
Download configurationconfig.json

Variables

Unnamed: 0
Real number (ℝ)

UNIFORM  UNIQUE 

Distinct425119
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean212559
Minimum0
Maximum425118
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size3.2 MiB
2024-03-10T23:10:52.384842image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile21255.9
Q1106279.5
median212559
Q3318838.5
95-th percentile403862.1
Maximum425118
Range425118
Interquartile range (IQR)212559

Descriptive statistics

Standard deviation122721.43
Coefficient of variation (CV)0.57735231
Kurtosis-1.2
Mean212559
Median Absolute Deviation (MAD)106280
Skewness0
Sum9.036287 × 1010
Variance1.5060549 × 1010
MonotonicityStrictly increasing
2024-03-10T23:10:52.724506image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1
 
< 0.1%
283409 1
 
< 0.1%
283419 1
 
< 0.1%
283418 1
 
< 0.1%
283417 1
 
< 0.1%
283416 1
 
< 0.1%
283415 1
 
< 0.1%
283414 1
 
< 0.1%
283413 1
 
< 0.1%
283412 1
 
< 0.1%
Other values (425109) 425109
> 99.9%
ValueCountFrequency (%)
0 1
< 0.1%
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
ValueCountFrequency (%)
425118 1
< 0.1%
425117 1
< 0.1%
425116 1
< 0.1%
425115 1
< 0.1%
425114 1
< 0.1%
425113 1
< 0.1%
425112 1
< 0.1%
425111 1
< 0.1%
425110 1
< 0.1%
425109 1
< 0.1%

Match ID
Real number (ℝ)

Distinct1842
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1089415
Minimum211028
Maximum1393328
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 MiB
2024-03-10T23:10:53.146785image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum211028
5-th percentile366622
Q1951373
median1233980
Q31310948
95-th percentile1379589
Maximum1393328
Range1182300
Interquartile range (IQR)359575

Descriptive statistics

Standard deviation322405.16
Coefficient of variation (CV)0.29594339
Kurtosis0.23327191
Mean1089415
Median Absolute Deviation (MAD)93251
Skewness-1.2743517
Sum4.63131 × 1011
Variance1.0394509 × 1011
MonotonicityNot monotonic
2024-03-10T23:10:53.492652image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1321260 275
 
0.1%
1275268 273
 
0.1%
1310175 271
 
0.1%
1275272 268
 
0.1%
1310171 268
 
0.1%
1282272 267
 
0.1%
1310177 266
 
0.1%
1275277 265
 
0.1%
1310174 264
 
0.1%
1310185 264
 
0.1%
Other values (1832) 422438
99.4%
ValueCountFrequency (%)
211028 215
0.1%
211048 245
0.1%
222678 233
0.1%
225263 238
0.1%
225271 253
0.1%
226374 239
0.1%
238195 249
0.1%
249227 249
0.1%
251487 160
< 0.1%
251488 230
0.1%
ValueCountFrequency (%)
1393328 225
0.1%
1393312 222
0.1%
1393311 247
0.1%
1393310 240
0.1%
1393309 219
0.1%
1391337 260
0.1%
1391336 243
0.1%
1391335 208
< 0.1%
1391333 244
0.1%
1391332 253
0.1%

Date
Date

Distinct1115
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size3.2 MiB
Minimum2005-02-17 00:00:00
Maximum2023-08-22 00:00:00
2024-03-10T23:10:53.820797image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T23:10:54.299427image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Venue
Text

Distinct183
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.2 MiB
2024-03-10T23:10:54.741031image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length60
Median length39
Mean length24.291961
Min length6

Characters and Unicode

Total characters10326974
Distinct characters58
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSuperSport Park
2nd rowSuperSport Park
3rd rowSuperSport Park
4th rowSuperSport Park
5th rowSuperSport Park
ValueCountFrequency (%)
cricket 182367
 
12.7%
stadium 157247
 
10.9%
ground 106925
 
7.4%
club 53281
 
3.7%
international 52349
 
3.6%
oval 40423
 
2.8%
park 35514
 
2.5%
sports 34451
 
2.4%
national 27530
 
1.9%
turf 21361
 
1.5%
Other values (259) 729052
50.6%
2024-03-10T23:10:55.429506image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1015381
 
9.8%
a 978697
 
9.5%
r 759924
 
7.4%
i 709427
 
6.9%
e 707191
 
6.8%
t 670326
 
6.5%
n 612134
 
5.9%
d 456405
 
4.4%
u 417411
 
4.0%
o 384768
 
3.7%
Other values (48) 3615310
35.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 7748285
75.0%
Uppercase Letter 1484079
 
14.4%
Space Separator 1015381
 
9.8%
Other Punctuation 19332
 
0.2%
Decimal Number 18965
 
0.2%
Close Punctuation 17794
 
0.2%
Open Punctuation 17794
 
0.2%
Dash Punctuation 5344
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 978697
12.6%
r 759924
9.8%
i 709427
9.2%
e 707191
9.1%
t 670326
 
8.7%
n 612134
 
7.9%
d 456405
 
5.9%
u 417411
 
5.4%
o 384768
 
5.0%
l 348728
 
4.5%
Other values (15) 1703274
22.0%
Uppercase Letter
ValueCountFrequency (%)
C 285318
19.2%
S 282919
19.1%
G 153329
10.3%
A 91304
 
6.2%
I 75995
 
5.1%
P 67952
 
4.6%
O 61280
 
4.1%
M 59190
 
4.0%
B 55301
 
3.7%
R 49229
 
3.3%
Other values (15) 302262
20.4%
Other Punctuation
ValueCountFrequency (%)
. 15068
77.9%
' 4264
 
22.1%
Decimal Number
ValueCountFrequency (%)
1 14884
78.5%
2 4081
 
21.5%
Space Separator
ValueCountFrequency (%)
1015381
100.0%
Close Punctuation
ValueCountFrequency (%)
) 17794
100.0%
Open Punctuation
ValueCountFrequency (%)
( 17794
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5344
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 9232364
89.4%
Common 1094610
 
10.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 978697
 
10.6%
r 759924
 
8.2%
i 709427
 
7.7%
e 707191
 
7.7%
t 670326
 
7.3%
n 612134
 
6.6%
d 456405
 
4.9%
u 417411
 
4.5%
o 384768
 
4.2%
l 348728
 
3.8%
Other values (40) 3187353
34.5%
Common
ValueCountFrequency (%)
1015381
92.8%
) 17794
 
1.6%
( 17794
 
1.6%
. 15068
 
1.4%
1 14884
 
1.4%
- 5344
 
0.5%
' 4264
 
0.4%
2 4081
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10326974
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1015381
 
9.8%
a 978697
 
9.5%
r 759924
 
7.4%
i 709427
 
6.9%
e 707191
 
6.8%
t 670326
 
6.5%
n 612134
 
5.9%
d 456405
 
4.4%
u 417411
 
4.0%
o 384768
 
3.7%
Other values (48) 3615310
35.0%
Distinct96
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.2 MiB
2024-03-10T23:10:55.801224image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length24
Median length16
Mean length8.5935515
Min length4

Characters and Unicode

Total characters3653282
Distinct characters51
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWest Indies
2nd rowWest Indies
3rd rowWest Indies
4th rowWest Indies
5th rowWest Indies
ValueCountFrequency (%)
new 27447
 
5.0%
pakistan 26344
 
4.8%
india 25358
 
4.6%
zealand 23649
 
4.3%
south 21480
 
3.9%
africa 21288
 
3.8%
sri 20666
 
3.7%
lanka 20666
 
3.7%
west 18180
 
3.3%
indies 18180
 
3.3%
Other values (102) 330043
59.6%
2024-03-10T23:10:56.393029image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 607506
16.6%
n 345027
 
9.4%
i 263367
 
7.2%
e 248854
 
6.8%
t 170457
 
4.7%
d 167943
 
4.6%
r 166789
 
4.6%
s 161395
 
4.4%
l 150227
 
4.1%
128182
 
3.5%
Other values (41) 1243535
34.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2974501
81.4%
Uppercase Letter 550599
 
15.1%
Space Separator 128182
 
3.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 607506
20.4%
n 345027
11.6%
i 263367
8.9%
e 248854
8.4%
t 170457
 
5.7%
d 167943
 
5.6%
r 166789
 
5.6%
s 161395
 
5.4%
l 150227
 
5.1%
g 87021
 
2.9%
Other values (16) 605915
20.4%
Uppercase Letter
ValueCountFrequency (%)
A 68419
12.4%
S 65867
12.0%
I 63145
11.5%
N 53249
9.7%
Z 37818
 
6.9%
P 33519
 
6.1%
B 32590
 
5.9%
E 27725
 
5.0%
L 25004
 
4.5%
W 18180
 
3.3%
Other values (14) 125083
22.7%
Space Separator
ValueCountFrequency (%)
128182
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3525100
96.5%
Common 128182
 
3.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 607506
17.2%
n 345027
 
9.8%
i 263367
 
7.5%
e 248854
 
7.1%
t 170457
 
4.8%
d 167943
 
4.8%
r 166789
 
4.7%
s 161395
 
4.6%
l 150227
 
4.3%
g 87021
 
2.5%
Other values (40) 1156514
32.8%
Common
ValueCountFrequency (%)
128182
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3653282
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 607506
16.6%
n 345027
 
9.4%
i 263367
 
7.2%
e 248854
 
6.8%
t 170457
 
4.7%
d 167943
 
4.6%
r 166789
 
4.6%
s 161395
 
4.4%
l 150227
 
4.1%
128182
 
3.5%
Other values (41) 1243535
34.0%
Distinct95
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.2 MiB
2024-03-10T23:10:57.495295image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length24
Median length16
Mean length8.6098128
Min length4

Characters and Unicode

Total characters3660195
Distinct characters52
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSouth Africa
2nd rowSouth Africa
3rd rowSouth Africa
4th rowSouth Africa
5th rowSouth Africa
ValueCountFrequency (%)
new 23559
 
4.3%
pakistan 23221
 
4.2%
australia 21785
 
3.9%
west 21405
 
3.9%
indies 21405
 
3.9%
england 21298
 
3.8%
india 19942
 
3.6%
sri 18523
 
3.3%
lanka 18523
 
3.3%
ireland 18482
 
3.3%
Other values (103) 345475
62.4%
2024-03-10T23:10:58.190146image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 581119
15.9%
n 346549
 
9.5%
e 271876
 
7.4%
i 253556
 
6.9%
d 175439
 
4.8%
r 169506
 
4.6%
t 165513
 
4.5%
l 165140
 
4.5%
s 163809
 
4.5%
128499
 
3.5%
Other values (42) 1239189
33.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2979060
81.4%
Uppercase Letter 552636
 
15.1%
Space Separator 128499
 
3.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 581119
19.5%
n 346549
11.6%
e 271876
9.1%
i 253556
8.5%
d 175439
 
5.9%
r 169506
 
5.7%
t 165513
 
5.6%
l 165140
 
5.5%
s 163809
 
5.5%
g 89891
 
3.0%
Other values (16) 596662
20.0%
Uppercase Letter
ValueCountFrequency (%)
I 68079
12.3%
S 66493
12.0%
A 62327
11.3%
N 49028
 
8.9%
P 34085
 
6.2%
B 33474
 
6.1%
Z 31294
 
5.7%
E 29872
 
5.4%
L 25382
 
4.6%
W 22378
 
4.0%
Other values (15) 130224
23.6%
Space Separator
ValueCountFrequency (%)
128499
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3531696
96.5%
Common 128499
 
3.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 581119
16.5%
n 346549
 
9.8%
e 271876
 
7.7%
i 253556
 
7.2%
d 175439
 
5.0%
r 169506
 
4.8%
t 165513
 
4.7%
l 165140
 
4.7%
s 163809
 
4.6%
g 89891
 
2.5%
Other values (41) 1149298
32.5%
Common
ValueCountFrequency (%)
128499
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3660195
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 581119
15.9%
n 346549
 
9.5%
e 271876
 
7.4%
i 253556
 
6.9%
d 175439
 
4.8%
r 169506
 
4.6%
t 165513
 
4.5%
l 165140
 
4.5%
s 163809
 
4.5%
128499
 
3.5%
Other values (42) 1239189
33.9%

Innings
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.2 MiB
1
224815 
2
200304 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters425119
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 224815
52.9%
2 200304
47.1%

Length

2024-03-10T23:10:58.424513image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-10T23:10:58.627628image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1 224815
52.9%
2 200304
47.1%

Most occurring characters

ValueCountFrequency (%)
1 224815
52.9%
2 200304
47.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 425119
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 224815
52.9%
2 200304
47.1%

Most occurring scripts

ValueCountFrequency (%)
Common 425119
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 224815
52.9%
2 200304
47.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 425119
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 224815
52.9%
2 200304
47.1%

Over
Real number (ℝ)

Distinct20
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.9593949
Minimum1
Maximum20
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 MiB
2024-03-10T23:10:58.815121image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q15
median10
Q315
95-th percentile19
Maximum20
Range19
Interquartile range (IQR)10

Descriptive statistics

Standard deviation5.6331341
Coefficient of variation (CV)0.56561007
Kurtosis-1.1629064
Mean9.9593949
Median Absolute Deviation (MAD)5
Skewness0.088803982
Sum4233928
Variance31.732199
MonotonicityNot monotonic
2024-03-10T23:10:59.027852image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
1 23529
 
5.5%
2 23423
 
5.5%
3 23109
 
5.4%
4 23028
 
5.4%
5 23008
 
5.4%
6 22844
 
5.4%
7 22578
 
5.3%
8 22415
 
5.3%
9 22279
 
5.2%
10 22166
 
5.2%
Other values (10) 196740
46.3%
ValueCountFrequency (%)
1 23529
5.5%
2 23423
5.5%
3 23109
5.4%
4 23028
5.4%
5 23008
5.4%
6 22844
5.4%
7 22578
5.3%
8 22415
5.3%
9 22279
5.2%
10 22166
5.2%
ValueCountFrequency (%)
20 14539
3.4%
19 17170
4.0%
18 18536
4.4%
17 19517
4.6%
16 20098
4.7%
15 20765
4.9%
14 21076
5.0%
13 21415
5.0%
12 21660
5.1%
11 21964
5.2%

Ball
Real number (ℝ)

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.4863768
Minimum0
Maximum7
Zeros69
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size3.2 MiB
2024-03-10T23:10:59.259862image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q35
95-th percentile6
Maximum7
Range7
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.7089028
Coefficient of variation (CV)0.49016585
Kurtosis-1.2652852
Mean3.4863768
Median Absolute Deviation (MAD)1
Skewness0.010473644
Sum1482125
Variance2.9203488
MonotonicityNot monotonic
2024-03-10T23:10:59.469983image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
1 71722
16.9%
2 71229
16.8%
3 70974
16.7%
4 70758
16.6%
5 70369
16.6%
6 69840
16.4%
7 158
 
< 0.1%
0 69
 
< 0.1%
ValueCountFrequency (%)
0 69
 
< 0.1%
1 71722
16.9%
2 71229
16.8%
3 70974
16.7%
4 70758
16.6%
5 70369
16.6%
6 69840
16.4%
7 158
 
< 0.1%
ValueCountFrequency (%)
7 158
 
< 0.1%
6 69840
16.4%
5 70369
16.6%
4 70758
16.6%
3 70974
16.7%
2 71229
16.8%
1 71722
16.9%
0 69
 
< 0.1%

Batter
Text

Distinct2899
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size3.2 MiB
2024-03-10T23:10:59.887012image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length22
Median length19
Mean length10.501064
Min length5

Characters and Unicode

Total characters4464202
Distinct characters60
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique65 ?
Unique (%)< 0.1%

Sample

1st rowBA King
2nd rowKR Mayers
3rd rowBA King
4th rowJ Charles
5th rowJ Charles
ValueCountFrequency (%)
s 11003
 
1.3%
mohammad 10836
 
1.3%
a 8786
 
1.0%
singh 7727
 
0.9%
khan 7125
 
0.8%
v 6695
 
0.8%
r 6416
 
0.7%
ali 6324
 
0.7%
ahmed 6239
 
0.7%
j 5916
 
0.7%
Other values (3163) 786938
91.1%
2024-03-10T23:11:00.629518image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 546150
 
12.2%
438886
 
9.8%
i 244396
 
5.5%
e 225147
 
5.0%
n 209900
 
4.7%
r 203889
 
4.6%
h 192152
 
4.3%
l 168608
 
3.8%
m 142355
 
3.2%
o 137400
 
3.1%
Other values (50) 1955319
43.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2908578
65.2%
Uppercase Letter 1108662
 
24.8%
Space Separator 438886
 
9.8%
Dash Punctuation 3910
 
0.1%
Other Punctuation 3473
 
0.1%
Open Punctuation 231
 
< 0.1%
Close Punctuation 231
 
< 0.1%
Decimal Number 231
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 546150
18.8%
i 244396
 
8.4%
e 225147
 
7.7%
n 209900
 
7.2%
r 203889
 
7.0%
h 192152
 
6.6%
l 168608
 
5.8%
m 142355
 
4.9%
o 137400
 
4.7%
s 134597
 
4.6%
Other values (16) 703984
24.2%
Uppercase Letter
ValueCountFrequency (%)
S 129423
 
11.7%
M 118631
 
10.7%
A 109036
 
9.8%
R 70403
 
6.4%
J 67250
 
6.1%
D 64846
 
5.8%
K 60681
 
5.5%
B 55072
 
5.0%
P 53606
 
4.8%
C 50682
 
4.6%
Other values (16) 329032
29.7%
Decimal Number
ValueCountFrequency (%)
2 154
66.7%
3 76
32.9%
1 1
 
0.4%
Space Separator
ValueCountFrequency (%)
438886
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3910
100.0%
Other Punctuation
ValueCountFrequency (%)
' 3473
100.0%
Open Punctuation
ValueCountFrequency (%)
( 231
100.0%
Close Punctuation
ValueCountFrequency (%)
) 231
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 4017240
90.0%
Common 446962
 
10.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 546150
 
13.6%
i 244396
 
6.1%
e 225147
 
5.6%
n 209900
 
5.2%
r 203889
 
5.1%
h 192152
 
4.8%
l 168608
 
4.2%
m 142355
 
3.5%
o 137400
 
3.4%
s 134597
 
3.4%
Other values (42) 1812646
45.1%
Common
ValueCountFrequency (%)
438886
98.2%
- 3910
 
0.9%
' 3473
 
0.8%
( 231
 
0.1%
) 231
 
0.1%
2 154
 
< 0.1%
3 76
 
< 0.1%
1 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4464202
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 546150
 
12.2%
438886
 
9.8%
i 244396
 
5.5%
e 225147
 
5.0%
n 209900
 
4.7%
r 203889
 
4.6%
h 192152
 
4.3%
l 168608
 
3.8%
m 142355
 
3.2%
o 137400
 
3.1%
Other values (50) 1955319
43.8%
Distinct2854
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size3.2 MiB
2024-03-10T23:11:01.045047image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length22
Median length19
Mean length10.506929
Min length5

Characters and Unicode

Total characters4466695
Distinct characters60
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique54 ?
Unique (%)< 0.1%

Sample

1st rowKR Mayers
2nd rowBA King
3rd rowKR Mayers
4th rowKR Mayers
5th rowKR Mayers
ValueCountFrequency (%)
s 11304
 
1.3%
mohammad 10778
 
1.2%
a 8866
 
1.0%
singh 7838
 
0.9%
khan 7127
 
0.8%
v 6643
 
0.8%
ali 6419
 
0.7%
r 6374
 
0.7%
ahmed 6105
 
0.7%
j 5873
 
0.7%
Other values (3119) 786950
91.1%
2024-03-10T23:11:01.988483image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 545937
 
12.2%
439158
 
9.8%
i 245201
 
5.5%
e 225324
 
5.0%
n 210654
 
4.7%
r 203533
 
4.6%
h 192111
 
4.3%
l 168282
 
3.8%
m 141935
 
3.2%
o 137610
 
3.1%
Other values (50) 1956950
43.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2909165
65.1%
Uppercase Letter 1109830
 
24.8%
Space Separator 439158
 
9.8%
Dash Punctuation 4152
 
0.1%
Other Punctuation 3664
 
0.1%
Open Punctuation 242
 
< 0.1%
Close Punctuation 242
 
< 0.1%
Decimal Number 242
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 545937
18.8%
i 245201
 
8.4%
e 225324
 
7.7%
n 210654
 
7.2%
r 203533
 
7.0%
h 192111
 
6.6%
l 168282
 
5.8%
m 141935
 
4.9%
o 137610
 
4.7%
s 134788
 
4.6%
Other values (16) 703790
24.2%
Uppercase Letter
ValueCountFrequency (%)
S 130032
 
11.7%
M 118695
 
10.7%
A 108759
 
9.8%
R 70669
 
6.4%
J 67213
 
6.1%
D 65274
 
5.9%
K 60496
 
5.5%
B 55285
 
5.0%
P 53266
 
4.8%
C 51180
 
4.6%
Other values (16) 328961
29.6%
Decimal Number
ValueCountFrequency (%)
2 179
74.0%
3 61
 
25.2%
1 2
 
0.8%
Space Separator
ValueCountFrequency (%)
439158
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4152
100.0%
Other Punctuation
ValueCountFrequency (%)
' 3664
100.0%
Open Punctuation
ValueCountFrequency (%)
( 242
100.0%
Close Punctuation
ValueCountFrequency (%)
) 242
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 4018995
90.0%
Common 447700
 
10.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 545937
 
13.6%
i 245201
 
6.1%
e 225324
 
5.6%
n 210654
 
5.2%
r 203533
 
5.1%
h 192111
 
4.8%
l 168282
 
4.2%
m 141935
 
3.5%
o 137610
 
3.4%
s 134788
 
3.4%
Other values (42) 1813620
45.1%
Common
ValueCountFrequency (%)
439158
98.1%
- 4152
 
0.9%
' 3664
 
0.8%
( 242
 
0.1%
) 242
 
0.1%
2 179
 
< 0.1%
3 61
 
< 0.1%
1 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4466695
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 545937
 
12.2%
439158
 
9.8%
i 245201
 
5.5%
e 225324
 
5.0%
n 210654
 
4.7%
r 203533
 
4.6%
h 192111
 
4.3%
l 168282
 
3.8%
m 141935
 
3.2%
o 137610
 
3.1%
Other values (50) 1956950
43.8%

Bowler
Text

Distinct2156
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.2 MiB
2024-03-10T23:11:02.356225image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length22
Median length19
Mean length10.544095
Min length5

Characters and Unicode

Total characters4482495
Distinct characters60
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)< 0.1%

Sample

1st rowWD Parnell
2nd rowWD Parnell
3rd rowWD Parnell
4th rowWD Parnell
5th rowWD Parnell
ValueCountFrequency (%)
khan 12256
 
1.4%
mohammad 11520
 
1.3%
a 10372
 
1.2%
s 9916
 
1.1%
m 7539
 
0.9%
b 7237
 
0.8%
r 6969
 
0.8%
singh 6702
 
0.8%
ahmed 6477
 
0.7%
j 6399
 
0.7%
Other values (2472) 781503
90.2%
2024-03-10T23:11:02.996827image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 571690
 
12.8%
441771
 
9.9%
e 239014
 
5.3%
i 227520
 
5.1%
n 213216
 
4.8%
h 209917
 
4.7%
r 202248
 
4.5%
l 148437
 
3.3%
m 139700
 
3.1%
o 137938
 
3.1%
Other values (50) 1951044
43.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2936631
65.5%
Uppercase Letter 1096775
 
24.5%
Space Separator 441771
 
9.9%
Dash Punctuation 4019
 
0.1%
Other Punctuation 1361
 
< 0.1%
Open Punctuation 646
 
< 0.1%
Close Punctuation 646
 
< 0.1%
Decimal Number 646
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 571690
19.5%
e 239014
 
8.1%
i 227520
 
7.7%
n 213216
 
7.3%
h 209917
 
7.1%
r 202248
 
6.9%
l 148437
 
5.1%
m 139700
 
4.8%
o 137938
 
4.7%
d 135882
 
4.6%
Other values (16) 711069
24.2%
Uppercase Letter
ValueCountFrequency (%)
S 127964
 
11.7%
A 119570
 
10.9%
M 115177
 
10.5%
J 70333
 
6.4%
R 67118
 
6.1%
K 61969
 
5.7%
B 54959
 
5.0%
C 49814
 
4.5%
D 48815
 
4.5%
H 46581
 
4.2%
Other values (16) 334475
30.5%
Decimal Number
ValueCountFrequency (%)
3 468
72.4%
2 104
 
16.1%
1 74
 
11.5%
Space Separator
ValueCountFrequency (%)
441771
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4019
100.0%
Other Punctuation
ValueCountFrequency (%)
' 1361
100.0%
Open Punctuation
ValueCountFrequency (%)
( 646
100.0%
Close Punctuation
ValueCountFrequency (%)
) 646
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 4033406
90.0%
Common 449089
 
10.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 571690
 
14.2%
e 239014
 
5.9%
i 227520
 
5.6%
n 213216
 
5.3%
h 209917
 
5.2%
r 202248
 
5.0%
l 148437
 
3.7%
m 139700
 
3.5%
o 137938
 
3.4%
d 135882
 
3.4%
Other values (42) 1807844
44.8%
Common
ValueCountFrequency (%)
441771
98.4%
- 4019
 
0.9%
' 1361
 
0.3%
( 646
 
0.1%
) 646
 
0.1%
3 468
 
0.1%
2 104
 
< 0.1%
1 74
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4482495
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 571690
 
12.8%
441771
 
9.9%
e 239014
 
5.3%
i 227520
 
5.1%
n 213216
 
4.8%
h 209917
 
4.7%
r 202248
 
4.5%
l 148437
 
3.3%
m 139700
 
3.1%
o 137938
 
3.1%
Other values (50) 1951044
43.5%

Batter Runs
Real number (ℝ)

ZEROS 

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1395021
Minimum0
Maximum7
Zeros184757
Zeros (%)43.5%
Negative0
Negative (%)0.0%
Memory size3.2 MiB
2024-03-10T23:11:03.211871image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile4
Maximum7
Range7
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.5460202
Coefficient of variation (CV)1.3567506
Kurtosis2.2399305
Mean1.1395021
Median Absolute Deviation (MAD)1
Skewness1.7191293
Sum484424
Variance2.3901783
MonotonicityNot monotonic
2024-03-10T23:11:03.399367image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 184757
43.5%
1 149518
35.2%
4 41055
 
9.7%
2 30469
 
7.2%
6 17212
 
4.0%
3 2034
 
0.5%
5 72
 
< 0.1%
7 2
 
< 0.1%
ValueCountFrequency (%)
0 184757
43.5%
1 149518
35.2%
2 30469
 
7.2%
3 2034
 
0.5%
4 41055
 
9.7%
5 72
 
< 0.1%
6 17212
 
4.0%
7 2
 
< 0.1%
ValueCountFrequency (%)
7 2
 
< 0.1%
6 17212
 
4.0%
5 72
 
< 0.1%
4 41055
 
9.7%
3 2034
 
0.5%
2 30469
 
7.2%
1 149518
35.2%
0 184757
43.5%

Extra Runs
Real number (ℝ)

ZEROS 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.075131904
Minimum0
Maximum6
Zeros399765
Zeros (%)94.0%
Negative0
Negative (%)0.0%
Memory size3.2 MiB
2024-03-10T23:11:03.589741image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum6
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.35930997
Coefficient of variation (CV)4.7823887
Kurtosis78.769069
Mean0.075131904
Median Absolute Deviation (MAD)0
Skewness7.6259703
Sum31940
Variance0.12910365
MonotonicityNot monotonic
2024-03-10T23:11:03.766925image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 399765
94.0%
1 22341
 
5.3%
2 1392
 
0.3%
4 833
 
0.2%
5 558
 
0.1%
3 229
 
0.1%
6 1
 
< 0.1%
ValueCountFrequency (%)
0 399765
94.0%
1 22341
 
5.3%
2 1392
 
0.3%
3 229
 
0.1%
4 833
 
0.2%
5 558
 
0.1%
6 1
 
< 0.1%
ValueCountFrequency (%)
6 1
 
< 0.1%
5 558
 
0.1%
4 833
 
0.2%
3 229
 
0.1%
2 1392
 
0.3%
1 22341
 
5.3%
0 399765
94.0%

Runs From Ball
Real number (ℝ)

ZEROS 

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.214634
Minimum0
Maximum8
Zeros160394
Zeros (%)37.7%
Negative0
Negative (%)0.0%
Memory size3.2 MiB
2024-03-10T23:11:04.016916image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile4
Maximum8
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.5360303
Coefficient of variation (CV)1.2646034
Kurtosis2.1415603
Mean1.214634
Median Absolute Deviation (MAD)1
Skewness1.6768463
Sum516364
Variance2.359389
MonotonicityNot monotonic
2024-03-10T23:11:04.229703image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
1 170365
40.1%
0 160394
37.7%
4 41659
 
9.8%
2 32230
 
7.6%
6 17107
 
4.0%
3 2392
 
0.6%
5 860
 
0.2%
7 111
 
< 0.1%
8 1
 
< 0.1%
ValueCountFrequency (%)
0 160394
37.7%
1 170365
40.1%
2 32230
 
7.6%
3 2392
 
0.6%
4 41659
 
9.8%
5 860
 
0.2%
6 17107
 
4.0%
7 111
 
< 0.1%
8 1
 
< 0.1%
ValueCountFrequency (%)
8 1
 
< 0.1%
7 111
 
< 0.1%
6 17107
 
4.0%
5 860
 
0.2%
4 41659
 
9.8%
3 2392
 
0.6%
2 32230
 
7.6%
1 170365
40.1%
0 160394
37.7%

Ball Rebowled
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.2 MiB
0
408034 
1
 
17085

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters425119
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 408034
96.0%
1 17085
 
4.0%

Length

2024-03-10T23:11:04.542195image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-10T23:11:04.870304image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
0 408034
96.0%
1 17085
 
4.0%

Most occurring characters

ValueCountFrequency (%)
0 408034
96.0%
1 17085
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 425119
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 408034
96.0%
1 17085
 
4.0%

Most occurring scripts

ValueCountFrequency (%)
Common 425119
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 408034
96.0%
1 17085
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 425119
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 408034
96.0%
1 17085
 
4.0%

Extra Type
Categorical

IMBALANCE 

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.2 MiB
[]
399765 
['wides']
 
15246
['legbyes']
 
6631
['noballs']
 
1775
['byes']
 
1631
Other values (5)
 
71

Length

Max length22
Median length2
Mean length2.4548115
Min length2

Characters and Unicode

Total characters1043587
Distinct characters19
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row[]
2nd row[]
3rd row[]
4th row[]
5th row[]

Common Values

ValueCountFrequency (%)
[] 399765
94.0%
['wides'] 15246
 
3.6%
['legbyes'] 6631
 
1.6%
['noballs'] 1775
 
0.4%
['byes'] 1631
 
0.4%
['byes', 'noballs'] 47
 
< 0.1%
['legbyes', 'noballs'] 11
 
< 0.1%
['penalty'] 7
 
< 0.1%
['noballs', 'byes'] 5
 
< 0.1%
['noballs', 'penalty'] 1
 
< 0.1%

Length

2024-03-10T23:11:05.213802image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-10T23:11:05.613806image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
399765
94.0%
wides 15246
 
3.6%
legbyes 6642
 
1.6%
noballs 1839
 
0.4%
byes 1683
 
0.4%
penalty 8
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
[ 425119
40.7%
] 425119
40.7%
' 50836
 
4.9%
e 30221
 
2.9%
s 25410
 
2.4%
w 15246
 
1.5%
i 15246
 
1.5%
d 15246
 
1.5%
l 10328
 
1.0%
b 10164
 
1.0%
Other values (9) 20652
 
2.0%

Most occurring categories

ValueCountFrequency (%)
Open Punctuation 425119
40.7%
Close Punctuation 425119
40.7%
Lowercase Letter 142385
 
13.6%
Other Punctuation 50900
 
4.9%
Space Separator 64
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 30221
21.2%
s 25410
17.8%
w 15246
10.7%
i 15246
10.7%
d 15246
10.7%
l 10328
 
7.3%
b 10164
 
7.1%
y 8333
 
5.9%
g 6642
 
4.7%
n 1847
 
1.3%
Other values (4) 3702
 
2.6%
Other Punctuation
ValueCountFrequency (%)
' 50836
99.9%
, 64
 
0.1%
Open Punctuation
ValueCountFrequency (%)
[ 425119
100.0%
Close Punctuation
ValueCountFrequency (%)
] 425119
100.0%
Space Separator
ValueCountFrequency (%)
64
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 901202
86.4%
Latin 142385
 
13.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 30221
21.2%
s 25410
17.8%
w 15246
10.7%
i 15246
10.7%
d 15246
10.7%
l 10328
 
7.3%
b 10164
 
7.1%
y 8333
 
5.9%
g 6642
 
4.7%
n 1847
 
1.3%
Other values (4) 3702
 
2.6%
Common
ValueCountFrequency (%)
[ 425119
47.2%
] 425119
47.2%
' 50836
 
5.6%
, 64
 
< 0.1%
64
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1043587
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
[ 425119
40.7%
] 425119
40.7%
' 50836
 
4.9%
e 30221
 
2.9%
s 25410
 
2.4%
w 15246
 
1.5%
i 15246
 
1.5%
d 15246
 
1.5%
l 10328
 
1.0%
b 10164
 
1.0%
Other values (9) 20652
 
2.0%

Wicket
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.2 MiB
0
401460 
1
 
23659

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters425119
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 401460
94.4%
1 23659
 
5.6%

Length

2024-03-10T23:11:05.998913image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-10T23:11:06.176591image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
0 401460
94.4%
1 23659
 
5.6%

Most occurring characters

ValueCountFrequency (%)
0 401460
94.4%
1 23659
 
5.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 425119
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 401460
94.4%
1 23659
 
5.6%

Most occurring scripts

ValueCountFrequency (%)
Common 425119
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 401460
94.4%
1 23659
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 425119
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 401460
94.4%
1 23659
 
5.6%

Method
Categorical

MISSING 

Distinct12
Distinct (%)0.1%
Missing401460
Missing (%)94.4%
Memory size3.2 MiB
caught
13378 
bowled
4917 
run out
1997 
lbw
1824 
stumped
 
796
Other values (7)
 
747

Length

Max length21
Median length6
Mean length6.2204658
Min length3

Characters and Unicode

Total characters147170
Distinct characters21
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowcaught
2nd rowcaught
3rd rowcaught
4th rowbowled
5th rowcaught

Common Values

ValueCountFrequency (%)
caught 13378
 
3.1%
bowled 4917
 
1.2%
run out 1997
 
0.5%
lbw 1824
 
0.4%
stumped 796
 
0.2%
caught and bowled 685
 
0.2%
retired hurt 27
 
< 0.1%
hit wicket 24
 
< 0.1%
retired not out 5
 
< 0.1%
retired out 3
 
< 0.1%
Other values (2) 3
 
< 0.1%
(Missing) 401460
94.4%

Length

2024-03-10T23:11:06.430393image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
caught 14063
51.9%
bowled 5602
 
20.7%
out 2005
 
7.4%
run 1997
 
7.4%
lbw 1824
 
6.7%
stumped 796
 
2.9%
and 685
 
2.5%
retired 35
 
0.1%
hurt 27
 
0.1%
hit 25
 
0.1%
Other values (7) 38
 
0.1%

Most occurring characters

ValueCountFrequency (%)
u 18890
12.8%
t 16988
11.5%
a 14749
10.0%
h 14118
9.6%
c 14090
9.6%
g 14065
9.6%
o 7614
 
5.2%
w 7451
 
5.1%
l 7430
 
5.0%
b 7429
 
5.0%
Other values (11) 24346
16.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 143732
97.7%
Space Separator 3438
 
2.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
u 18890
13.1%
t 16988
11.8%
a 14749
10.3%
h 14118
9.8%
c 14090
9.8%
g 14065
9.8%
o 7614
 
5.3%
w 7451
 
5.2%
l 7430
 
5.2%
b 7429
 
5.2%
Other values (10) 20908
14.5%
Space Separator
ValueCountFrequency (%)
3438
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 143732
97.7%
Common 3438
 
2.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
u 18890
13.1%
t 16988
11.8%
a 14749
10.3%
h 14118
9.8%
c 14090
9.8%
g 14065
9.8%
o 7614
 
5.3%
w 7451
 
5.2%
l 7430
 
5.2%
b 7429
 
5.2%
Other values (10) 20908
14.5%
Common
ValueCountFrequency (%)
3438
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 147170
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
u 18890
12.8%
t 16988
11.5%
a 14749
10.0%
h 14118
9.6%
c 14090
9.6%
g 14065
9.6%
o 7614
 
5.2%
w 7451
 
5.1%
l 7430
 
5.0%
b 7429
 
5.0%
Other values (11) 24346
16.5%

Player Out
Text

MISSING 

Distinct2683
Distinct (%)11.3%
Missing401460
Missing (%)94.4%
Memory size3.2 MiB
2024-03-10T23:11:07.126598image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length22
Median length19
Mean length10.539245
Min length5

Characters and Unicode

Total characters249348
Distinct characters60
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique498 ?
Unique (%)2.1%

Sample

1st rowBA King
2nd rowKR Mayers
3rd rowN Pooran
4th rowJ Charles
5th rowR Powell
ValueCountFrequency (%)
s 607
 
1.3%
mohammad 524
 
1.1%
a 509
 
1.1%
khan 466
 
1.0%
singh 403
 
0.8%
r 361
 
0.8%
ali 355
 
0.7%
j 327
 
0.7%
c 325
 
0.7%
ahmed 317
 
0.7%
Other values (2982) 43876
91.3%
2024-03-10T23:11:08.097200image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 30881
 
12.4%
24411
 
9.8%
i 13603
 
5.5%
e 12683
 
5.1%
n 11909
 
4.8%
r 11492
 
4.6%
h 10700
 
4.3%
l 9119
 
3.7%
m 7801
 
3.1%
o 7565
 
3.0%
Other values (50) 109184
43.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 163182
65.4%
Uppercase Letter 61338
 
24.6%
Space Separator 24411
 
9.8%
Dash Punctuation 194
 
0.1%
Other Punctuation 175
 
0.1%
Open Punctuation 16
 
< 0.1%
Close Punctuation 16
 
< 0.1%
Decimal Number 16
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 30881
18.9%
i 13603
 
8.3%
e 12683
 
7.8%
n 11909
 
7.3%
r 11492
 
7.0%
h 10700
 
6.6%
l 9119
 
5.6%
m 7801
 
4.8%
o 7565
 
4.6%
s 7555
 
4.6%
Other values (16) 39874
24.4%
Uppercase Letter
ValueCountFrequency (%)
S 7093
 
11.6%
M 6404
 
10.4%
A 6154
 
10.0%
R 3790
 
6.2%
J 3719
 
6.1%
D 3400
 
5.5%
K 3345
 
5.5%
B 2920
 
4.8%
C 2889
 
4.7%
P 2842
 
4.6%
Other values (16) 18782
30.6%
Decimal Number
ValueCountFrequency (%)
2 9
56.2%
3 6
37.5%
1 1
 
6.2%
Space Separator
ValueCountFrequency (%)
24411
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 194
100.0%
Other Punctuation
ValueCountFrequency (%)
' 175
100.0%
Open Punctuation
ValueCountFrequency (%)
( 16
100.0%
Close Punctuation
ValueCountFrequency (%)
) 16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 224520
90.0%
Common 24828
 
10.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 30881
 
13.8%
i 13603
 
6.1%
e 12683
 
5.6%
n 11909
 
5.3%
r 11492
 
5.1%
h 10700
 
4.8%
l 9119
 
4.1%
m 7801
 
3.5%
o 7565
 
3.4%
s 7555
 
3.4%
Other values (42) 101212
45.1%
Common
ValueCountFrequency (%)
24411
98.3%
- 194
 
0.8%
' 175
 
0.7%
( 16
 
0.1%
) 16
 
0.1%
2 9
 
< 0.1%
3 6
 
< 0.1%
1 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 249348
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 30881
 
12.4%
24411
 
9.8%
i 13603
 
5.5%
e 12683
 
5.1%
n 11909
 
4.8%
r 11492
 
4.6%
h 10700
 
4.3%
l 9119
 
3.7%
m 7801
 
3.1%
o 7565
 
3.0%
Other values (50) 109184
43.8%

Innings Runs
Real number (ℝ)

ZEROS 

Distinct267
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean68.763113
Minimum0
Maximum278
Zeros4580
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size3.2 MiB
2024-03-10T23:11:08.376335image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q131
median63
Q3101
95-th percentile152
Maximum278
Range278
Interquartile range (IQR)70

Descriptive statistics

Standard deviation46.234421
Coefficient of variation (CV)0.67237242
Kurtosis-0.31995701
Mean68.763113
Median Absolute Deviation (MAD)35
Skewness0.53290907
Sum29232506
Variance2137.6217
MonotonicityNot monotonic
2024-03-10T23:11:08.626327image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 5095
 
1.2%
0 4580
 
1.1%
4 3914
 
0.9%
6 3829
 
0.9%
2 3788
 
0.9%
5 3684
 
0.9%
8 3561
 
0.8%
12 3511
 
0.8%
7 3495
 
0.8%
37 3360
 
0.8%
Other values (257) 386302
90.9%
ValueCountFrequency (%)
0 4580
1.1%
1 5095
1.2%
2 3788
0.9%
3 2867
0.7%
4 3914
0.9%
5 3684
0.9%
6 3829
0.9%
7 3495
0.8%
8 3561
0.8%
9 3343
0.8%
ValueCountFrequency (%)
278 1
 
< 0.1%
274 1
 
< 0.1%
273 3
< 0.1%
267 1
 
< 0.1%
263 2
 
< 0.1%
261 1
 
< 0.1%
260 4
< 0.1%
259 3
< 0.1%
258 6
< 0.1%
257 2
 
< 0.1%

Innings Wickets
Real number (ℝ)

ZEROS 

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.7488797
Minimum0
Maximum11
Zeros77021
Zeros (%)18.1%
Negative0
Negative (%)0.0%
Memory size3.2 MiB
2024-03-10T23:11:08.846732image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q34
95-th percentile7
Maximum11
Range11
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.2839439
Coefficient of variation (CV)0.83086352
Kurtosis-0.10926061
Mean2.7488797
Median Absolute Deviation (MAD)2
Skewness0.75197668
Sum1168601
Variance5.2163997
MonotonicityNot monotonic
2024-03-10T23:11:09.034228image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 77021
18.1%
1 75621
17.8%
2 71616
16.8%
3 61227
14.4%
4 47693
11.2%
5 34795
8.2%
6 23728
 
5.6%
7 16108
 
3.8%
8 10256
 
2.4%
9 6393
 
1.5%
Other values (2) 661
 
0.2%
ValueCountFrequency (%)
0 77021
18.1%
1 75621
17.8%
2 71616
16.8%
3 61227
14.4%
4 47693
11.2%
5 34795
8.2%
6 23728
 
5.6%
7 16108
 
3.8%
8 10256
 
2.4%
9 6393
 
1.5%
ValueCountFrequency (%)
11 1
 
< 0.1%
10 660
 
0.2%
9 6393
 
1.5%
8 10256
 
2.4%
7 16108
 
3.8%
6 23728
 
5.6%
5 34795
8.2%
4 47693
11.2%
3 61227
14.4%
2 71616
16.8%

Target Score
Real number (ℝ)

Distinct212
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean153.29617
Minimum11
Maximum279
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 MiB
2024-03-10T23:11:09.264729image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile88
Q1129
median154
Q3179
95-th percentile214
Maximum279
Range268
Interquartile range (IQR)50

Descriptive statistics

Standard deviation38.138024
Coefficient of variation (CV)0.24878654
Kurtosis0.24095591
Mean153.29617
Median Absolute Deviation (MAD)25
Skewness-0.1126593
Sum65169115
Variance1454.5089
MonotonicityNot monotonic
2024-03-10T23:11:09.514720image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
154 7955
 
1.9%
142 6283
 
1.5%
148 6204
 
1.5%
165 6087
 
1.4%
169 5718
 
1.3%
161 5487
 
1.3%
159 5389
 
1.3%
160 5134
 
1.2%
147 5077
 
1.2%
145 5034
 
1.2%
Other values (202) 366751
86.3%
ValueCountFrequency (%)
11 55
 
< 0.1%
15 155
 
< 0.1%
24 100
 
< 0.1%
27 199
< 0.1%
30 149
 
< 0.1%
31 286
0.1%
37 109
 
< 0.1%
38 319
0.1%
40 96
 
< 0.1%
44 488
0.1%
ValueCountFrequency (%)
279 260
 
0.1%
264 246
 
0.1%
261 487
0.1%
259 514
0.1%
257 246
 
0.1%
255 250
 
0.1%
253 253
 
0.1%
249 250
 
0.1%
246 684
0.2%
244 483
0.1%

Runs to Get
Real number (ℝ)

MISSING 

Distinct295
Distinct (%)0.1%
Missing224815
Missing (%)52.9%
Infinite0
Infinite (%)0.0%
Mean90.19706
Minimum-39
Maximum273
Zeros443
Zeros (%)0.1%
Negative601
Negative (%)0.1%
Memory size3.2 MiB
2024-03-10T23:11:09.768352image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum-39
5-th percentile13
Q151
median88
Q3126
95-th percentile176
Maximum273
Range312
Interquartile range (IQR)75

Descriptive statistics

Standard deviation49.981172
Coefficient of variation (CV)0.55413305
Kurtosis-0.5304413
Mean90.19706
Median Absolute Deviation (MAD)37
Skewness0.27374588
Sum18066832
Variance2498.1175
MonotonicityNot monotonic
2024-03-10T23:11:10.004838image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
93 1487
 
0.3%
71 1468
 
0.3%
91 1450
 
0.3%
85 1443
 
0.3%
72 1440
 
0.3%
95 1428
 
0.3%
96 1427
 
0.3%
83 1421
 
0.3%
92 1418
 
0.3%
87 1416
 
0.3%
Other values (285) 185906
43.7%
(Missing) 224815
52.9%
ValueCountFrequency (%)
-39 1
 
< 0.1%
-35 1
 
< 0.1%
-29 1
 
< 0.1%
-24 1
 
< 0.1%
-23 1
 
< 0.1%
-22 1
 
< 0.1%
-20 1
 
< 0.1%
-19 1
 
< 0.1%
-18 4
< 0.1%
-17 2
< 0.1%
ValueCountFrequency (%)
273 2
 
< 0.1%
272 1
 
< 0.1%
271 1
 
< 0.1%
270 1
 
< 0.1%
269 2
 
< 0.1%
264 3
 
< 0.1%
263 1
 
< 0.1%
261 9
< 0.1%
260 4
< 0.1%
259 2
 
< 0.1%

Balls Remaining
Real number (ℝ)

Distinct122
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean62.797443
Minimum-1
Maximum120
Zeros2100
Zeros (%)0.5%
Negative8
Negative (%)< 0.1%
Memory size3.2 MiB
2024-03-10T23:11:10.259782image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile8
Q134
median64
Q392
95-th percentile114
Maximum120
Range121
Interquartile range (IQR)58

Descriptive statistics

Standard deviation33.835073
Coefficient of variation (CV)0.53879699
Kurtosis-1.1577359
Mean62.797443
Median Absolute Deviation (MAD)29
Skewness-0.086616374
Sum26696386
Variance1144.8122
MonotonicityNot monotonic
2024-03-10T23:11:10.509774image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
114 3995
 
0.9%
117 3943
 
0.9%
119 3936
 
0.9%
112 3913
 
0.9%
118 3908
 
0.9%
113 3898
 
0.9%
111 3883
 
0.9%
110 3878
 
0.9%
107 3870
 
0.9%
108 3868
 
0.9%
Other values (112) 386027
90.8%
ValueCountFrequency (%)
-1 8
 
< 0.1%
0 2100
0.5%
1 2302
0.5%
2 2366
0.6%
3 2485
0.6%
4 2543
0.6%
5 2589
0.6%
6 2729
0.6%
7 2776
0.7%
8 2823
0.7%
ValueCountFrequency (%)
120 343
 
0.1%
119 3936
0.9%
118 3908
0.9%
117 3943
0.9%
116 3851
0.9%
115 3864
0.9%
114 3995
0.9%
113 3898
0.9%
112 3913
0.9%
111 3883
0.9%

Winner
Text

Distinct89
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.2 MiB
2024-03-10T23:11:10.822262image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length24
Median length16
Mean length8.542688
Min length4

Characters and Unicode

Total characters3631659
Distinct characters52
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSouth Africa
2nd rowSouth Africa
3rd rowSouth Africa
4th rowSouth Africa
5th rowSouth Africa
ValueCountFrequency (%)
pakistan 31054
 
5.6%
india 29734
 
5.4%
new 27974
 
5.1%
zealand 23300
 
4.2%
south 21399
 
3.9%
africa 21399
 
3.9%
australia 20882
 
3.8%
england 20702
 
3.8%
sri 17536
 
3.2%
lanka 17536
 
3.2%
Other values (97) 319589
58.0%
2024-03-10T23:11:11.385971image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 607097
16.7%
n 355185
 
9.8%
i 261556
 
7.2%
e 242421
 
6.7%
t 175099
 
4.8%
d 174173
 
4.8%
s 161674
 
4.5%
r 156920
 
4.3%
l 154965
 
4.3%
125986
 
3.5%
Other values (42) 1216583
33.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2957480
81.4%
Uppercase Letter 548193
 
15.1%
Space Separator 125986
 
3.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 607097
20.5%
n 355185
12.0%
i 261556
8.8%
e 242421
 
8.2%
t 175099
 
5.9%
d 174173
 
5.9%
s 161674
 
5.5%
r 156920
 
5.3%
l 154965
 
5.2%
u 88662
 
3.0%
Other values (16) 579728
19.6%
Uppercase Letter
ValueCountFrequency (%)
A 72013
13.1%
I 67909
12.4%
S 63552
11.6%
N 55876
10.2%
P 39800
 
7.3%
Z 32122
 
5.9%
E 28607
 
5.2%
B 27100
 
4.9%
L 21607
 
3.9%
U 19334
 
3.5%
Other values (15) 120273
21.9%
Space Separator
ValueCountFrequency (%)
125986
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3505673
96.5%
Common 125986
 
3.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 607097
17.3%
n 355185
 
10.1%
i 261556
 
7.5%
e 242421
 
6.9%
t 175099
 
5.0%
d 174173
 
5.0%
s 161674
 
4.6%
r 156920
 
4.5%
l 154965
 
4.4%
u 88662
 
2.5%
Other values (41) 1127921
32.2%
Common
ValueCountFrequency (%)
125986
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3631659
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 607097
16.7%
n 355185
 
9.8%
i 261556
 
7.2%
e 242421
 
6.7%
t 175099
 
4.8%
d 174173
 
4.8%
s 161674
 
4.5%
r 156920
 
4.3%
l 154965
 
4.3%
125986
 
3.5%
Other values (42) 1216583
33.5%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.2 MiB
0
219364 
1
205755 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters425119
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 219364
51.6%
1 205755
48.4%

Length

2024-03-10T23:11:11.604713image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-10T23:11:11.776584image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
0 219364
51.6%
1 205755
48.4%

Most occurring characters

ValueCountFrequency (%)
0 219364
51.6%
1 205755
48.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 425119
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 219364
51.6%
1 205755
48.4%

Most occurring scripts

ValueCountFrequency (%)
Common 425119
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 219364
51.6%
1 205755
48.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 425119
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 219364
51.6%
1 205755
48.4%

Total Batter Runs
Real number (ℝ)

ZEROS 

Distinct160
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.71182
Minimum0
Maximum172
Zeros55592
Zeros (%)13.1%
Negative0
Negative (%)0.0%
Memory size3.2 MiB
2024-03-10T23:11:11.991023image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median9
Q321
95-th percentile49
Maximum172
Range172
Interquartile range (IQR)19

Descriptive statistics

Standard deviation16.64172
Coefficient of variation (CV)1.1311803
Kurtosis4.121421
Mean14.71182
Median Absolute Deviation (MAD)8
Skewness1.8050702
Sum6254274
Variance276.94685
MonotonicityNot monotonic
2024-03-10T23:11:12.245190image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 55592
 
13.1%
1 32066
 
7.5%
2 23262
 
5.5%
4 20041
 
4.7%
5 16694
 
3.9%
3 16098
 
3.8%
6 16081
 
3.8%
7 13576
 
3.2%
8 13214
 
3.1%
9 11884
 
2.8%
Other values (150) 206611
48.6%
ValueCountFrequency (%)
0 55592
13.1%
1 32066
7.5%
2 23262
5.5%
3 16098
 
3.8%
4 20041
 
4.7%
5 16694
 
3.9%
6 16081
 
3.8%
7 13576
 
3.2%
8 13214
 
3.1%
9 11884
 
2.8%
ValueCountFrequency (%)
172 1
< 0.1%
170 1
< 0.1%
168 1
< 0.1%
167 1
< 0.1%
166 1
< 0.1%
162 1
< 0.1%
160 1
< 0.1%
158 1
< 0.1%
157 1
< 0.1%
156 1
< 0.1%

Total Non Striker Runs
Real number (ℝ)

ZEROS 

Distinct143
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.952406
Minimum0
Maximum168
Zeros50366
Zeros (%)11.8%
Negative0
Negative (%)0.0%
Memory size3.2 MiB
2024-03-10T23:11:12.510805image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median8
Q320
95-th percentile47
Maximum168
Range168
Interquartile range (IQR)18

Descriptive statistics

Standard deviation15.976979
Coefficient of variation (CV)1.1451056
Kurtosis4.1507189
Mean13.952406
Median Absolute Deviation (MAD)7
Skewness1.8108751
Sum5931433
Variance255.26386
MonotonicityNot monotonic
2024-03-10T23:11:12.760797image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 50366
 
11.8%
1 42797
 
10.1%
2 26274
 
6.2%
3 18716
 
4.4%
4 17661
 
4.2%
5 16695
 
3.9%
6 15779
 
3.7%
7 13836
 
3.3%
8 13214
 
3.1%
9 11671
 
2.7%
Other values (133) 198110
46.6%
ValueCountFrequency (%)
0 50366
11.8%
1 42797
10.1%
2 26274
6.2%
3 18716
 
4.4%
4 17661
 
4.2%
5 16695
 
3.9%
6 15779
 
3.7%
7 13836
 
3.3%
8 13214
 
3.1%
9 11671
 
2.7%
ValueCountFrequency (%)
168 2
 
< 0.1%
167 1
 
< 0.1%
158 8
< 0.1%
157 2
 
< 0.1%
148 1
 
< 0.1%
145 6
< 0.1%
144 2
 
< 0.1%
143 1
 
< 0.1%
142 4
< 0.1%
139 2
 
< 0.1%

Batter Balls Faced
Real number (ℝ)

ZEROS 

Distinct77
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.48656
Minimum0
Maximum76
Zeros25027
Zeros (%)5.9%
Negative0
Negative (%)0.0%
Memory size3.2 MiB
2024-03-10T23:11:13.026250image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14
median9
Q318
95-th percentile36
Maximum76
Range76
Interquartile range (IQR)14

Descriptive statistics

Standard deviation11.475981
Coefficient of variation (CV)0.91906667
Kurtosis1.4678768
Mean12.48656
Median Absolute Deviation (MAD)6
Skewness1.2960818
Sum5308274
Variance131.69815
MonotonicityNot monotonic
2024-03-10T23:11:13.292018image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 26817
 
6.3%
1 26760
 
6.3%
0 25027
 
5.9%
3 24858
 
5.8%
4 23035
 
5.4%
5 21334
 
5.0%
6 19838
 
4.7%
7 18383
 
4.3%
8 17087
 
4.0%
9 15895
 
3.7%
Other values (67) 206085
48.5%
ValueCountFrequency (%)
0 25027
5.9%
1 26760
6.3%
2 26817
6.3%
3 24858
5.8%
4 23035
5.4%
5 21334
5.0%
6 19838
4.7%
7 18383
4.3%
8 17087
4.0%
9 15895
3.7%
ValueCountFrequency (%)
76 2
 
< 0.1%
75 2
 
< 0.1%
74 3
 
< 0.1%
73 3
 
< 0.1%
72 3
 
< 0.1%
71 4
 
< 0.1%
70 8
 
< 0.1%
69 11
< 0.1%
68 16
< 0.1%
67 21
< 0.1%

Non Striker Balls Faced
Real number (ℝ)

ZEROS 

Distinct73
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.138455
Minimum0
Maximum74
Zeros33793
Zeros (%)7.9%
Negative0
Negative (%)0.0%
Memory size3.2 MiB
2024-03-10T23:11:13.526385image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median9
Q318
95-th percentile36
Maximum74
Range74
Interquartile range (IQR)15

Descriptive statistics

Standard deviation11.336384
Coefficient of variation (CV)0.9339231
Kurtosis1.392277
Mean12.138455
Median Absolute Deviation (MAD)7
Skewness1.2770025
Sum5160288
Variance128.5136
MonotonicityNot monotonic
2024-03-10T23:11:13.792001image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 33793
 
7.9%
1 26576
 
6.3%
2 25814
 
6.1%
3 23981
 
5.6%
4 22082
 
5.2%
5 20887
 
4.9%
6 20264
 
4.8%
7 18036
 
4.2%
8 16604
 
3.9%
9 15128
 
3.6%
Other values (63) 201954
47.5%
ValueCountFrequency (%)
0 33793
7.9%
1 26576
6.3%
2 25814
6.1%
3 23981
5.6%
4 22082
5.2%
5 20887
4.9%
6 20264
4.8%
7 18036
4.2%
8 16604
3.9%
9 15128
3.6%
ValueCountFrequency (%)
74 2
 
< 0.1%
73 2
 
< 0.1%
70 2
 
< 0.1%
69 4
 
< 0.1%
68 17
 
< 0.1%
67 14
 
< 0.1%
66 14
 
< 0.1%
65 29
< 0.1%
64 49
< 0.1%
63 44
< 0.1%

Player Out Runs
Real number (ℝ)

MISSING 

Distinct123
Distinct (%)0.5%
Missing401460
Missing (%)94.4%
Infinite0
Infinite (%)0.0%
Mean15.38628
Minimum0
Maximum172
Zeros2955
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size3.2 MiB
2024-03-10T23:11:14.053994image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median9
Q322
95-th percentile52
Maximum172
Range172
Interquartile range (IQR)20

Descriptive statistics

Standard deviation17.408768
Coefficient of variation (CV)1.1314475
Kurtosis3.9897709
Mean15.38628
Median Absolute Deviation (MAD)8
Skewness1.80077
Sum364024
Variance303.06521
MonotonicityNot monotonic
2024-03-10T23:11:14.525885image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2955
 
0.7%
1 1695
 
0.4%
2 1292
 
0.3%
4 1092
 
0.3%
5 943
 
0.2%
3 889
 
0.2%
6 855
 
0.2%
7 786
 
0.2%
8 722
 
0.2%
9 679
 
0.2%
Other values (113) 11751
 
2.8%
(Missing) 401460
94.4%
ValueCountFrequency (%)
0 2955
0.7%
1 1695
0.4%
2 1292
0.3%
3 889
 
0.2%
4 1092
 
0.3%
5 943
 
0.2%
6 855
 
0.2%
7 786
 
0.2%
8 722
 
0.2%
9 679
 
0.2%
ValueCountFrequency (%)
172 1
 
< 0.1%
156 1
 
< 0.1%
132 1
 
< 0.1%
124 1
 
< 0.1%
123 1
 
< 0.1%
122 2
< 0.1%
119 1
 
< 0.1%
118 2
< 0.1%
117 3
< 0.1%
115 1
 
< 0.1%

Player Out Balls Faced
Real number (ℝ)

MISSING 

Distinct69
Distinct (%)0.3%
Missing401460
Missing (%)94.4%
Infinite0
Infinite (%)0.0%
Mean13.853502
Minimum0
Maximum76
Zeros103
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size3.2 MiB
2024-03-10T23:11:14.775878image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q15
median10
Q320
95-th percentile39
Maximum76
Range76
Interquartile range (IQR)15

Descriptive statistics

Standard deviation11.983127
Coefficient of variation (CV)0.864989
Kurtosis1.3482991
Mean13.853502
Median Absolute Deviation (MAD)7
Skewness1.292813
Sum327760
Variance143.59533
MonotonicityNot monotonic
2024-03-10T23:11:15.030726image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 1454
 
0.3%
2 1427
 
0.3%
4 1374
 
0.3%
1 1372
 
0.3%
5 1239
 
0.3%
6 1171
 
0.3%
7 1152
 
0.3%
8 986
 
0.2%
9 940
 
0.2%
10 865
 
0.2%
Other values (59) 11679
 
2.7%
(Missing) 401460
94.4%
ValueCountFrequency (%)
0 103
 
< 0.1%
1 1372
0.3%
2 1427
0.3%
3 1454
0.3%
4 1374
0.3%
5 1239
0.3%
6 1171
0.3%
7 1152
0.3%
8 986
0.2%
9 940
0.2%
ValueCountFrequency (%)
76 1
 
< 0.1%
70 1
 
< 0.1%
67 6
< 0.1%
66 5
 
< 0.1%
65 2
 
< 0.1%
63 3
 
< 0.1%
62 13
< 0.1%
61 5
 
< 0.1%
60 6
< 0.1%
59 13
< 0.1%

Bowler Runs Conceded
Real number (ℝ)

ZEROS 

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1877098
Minimum0
Maximum8
Zeros168659
Zeros (%)39.7%
Negative0
Negative (%)0.0%
Memory size3.2 MiB
2024-03-10T23:11:15.244808image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile4
Maximum8
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.5394593
Coefficient of variation (CV)1.2961578
Kurtosis2.1972925
Mean1.1877098
Median Absolute Deviation (MAD)1
Skewness1.6961972
Sum504918
Variance2.3699348
MonotonicityNot monotonic
2024-03-10T23:11:15.447930image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 168659
39.7%
1 163553
38.5%
4 40854
 
9.6%
2 31659
 
7.4%
6 17104
 
4.0%
3 2333
 
0.5%
5 846
 
0.2%
7 110
 
< 0.1%
8 1
 
< 0.1%
ValueCountFrequency (%)
0 168659
39.7%
1 163553
38.5%
2 31659
 
7.4%
3 2333
 
0.5%
4 40854
 
9.6%
5 846
 
0.2%
6 17104
 
4.0%
7 110
 
< 0.1%
8 1
 
< 0.1%
ValueCountFrequency (%)
8 1
 
< 0.1%
7 110
 
< 0.1%
6 17104
 
4.0%
5 846
 
0.2%
4 40854
 
9.6%
3 2333
 
0.5%
2 31659
 
7.4%
1 163553
38.5%
0 168659
39.7%

Valid Ball
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.2 MiB
1
408034 
0
 
17085

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters425119
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 408034
96.0%
0 17085
 
4.0%

Length

2024-03-10T23:11:15.666671image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-10T23:11:15.835978image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1 408034
96.0%
0 17085
 
4.0%

Most occurring characters

ValueCountFrequency (%)
1 408034
96.0%
0 17085
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 425119
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 408034
96.0%
0 17085
 
4.0%

Most occurring scripts

ValueCountFrequency (%)
Common 425119
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 408034
96.0%
0 17085
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 425119
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 408034
96.0%
0 17085
 
4.0%

Interactions

2024-03-10T23:10:41.016287image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T23:09:13.997934image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T23:09:18.832098image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T23:09:23.737505image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T23:09:28.958455image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T23:09:33.881527image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T23:09:38.870331image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T23:09:43.925653image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
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2024-03-10T23:10:35.596038image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T23:10:39.201678image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T23:10:43.991948image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T23:09:16.851857image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T23:09:21.691562image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T23:09:26.730263image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T23:09:31.835704image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T23:09:36.791072image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T23:09:41.731163image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T23:09:46.868492image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T23:09:51.741870image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T23:09:56.647676image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T23:10:01.853482image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T23:10:06.270306image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T23:10:10.966544image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T23:10:16.140037image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T23:10:21.158352image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T23:10:26.214917image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T23:10:31.451980image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T23:10:35.775108image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T23:10:39.387062image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T23:10:44.457639image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T23:09:17.117356image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T23:09:21.973321image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T23:09:26.999362image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T23:09:32.106721image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T23:09:37.076365image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T23:09:41.996403image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T23:09:47.154794image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T23:09:52.007222image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T23:09:56.896774image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T23:10:02.120640image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T23:10:06.494413image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T23:10:11.231504image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T23:10:16.403512image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T23:10:21.441226image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T23:10:26.481179image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T23:10:31.746699image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T23:10:35.964153image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T23:10:39.579998image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T23:10:44.723951image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T23:09:17.384973image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T23:09:22.240132image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T23:09:27.278837image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T23:09:32.384241image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T23:09:37.356980image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T23:09:42.278054image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T23:09:47.418103image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T23:09:52.274111image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T23:09:57.163667image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T23:10:02.368934image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T23:10:06.727223image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T23:10:11.514084image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T23:10:16.670826image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T23:10:21.709754image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T23:10:26.764116image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T23:10:32.050430image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T23:10:36.148740image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T23:10:39.755317image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T23:10:45.004664image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T23:09:17.650722image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T23:09:22.523144image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T23:09:27.561561image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T23:09:32.666543image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T23:09:37.638884image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T23:09:42.561878image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T23:09:47.685006image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T23:09:52.555723image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T23:09:57.445385image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T23:10:02.651143image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T23:10:06.943561image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T23:10:11.780290image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T23:10:16.951905image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T23:10:22.006763image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T23:10:27.046706image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T23:10:32.415541image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T23:10:36.343545image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T23:10:39.958948image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T23:10:45.273696image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T23:09:17.916249image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T23:09:22.790727image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T23:09:27.828764image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T23:09:32.948950image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T23:09:37.906216image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T23:09:42.828316image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T23:09:47.982072image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T23:09:52.820847image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T23:09:57.711818image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T23:10:02.916597image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T23:10:07.176301image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T23:10:12.062130image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T23:10:17.233409image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T23:10:22.273434image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T23:10:27.343068image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T23:10:32.701022image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T23:10:36.535302image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T23:10:40.141846image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T23:10:45.489921image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T23:09:18.116460image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T23:09:22.992088image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T23:09:28.027544image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T23:09:33.146746image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T23:09:38.108301image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T23:09:43.025157image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T23:09:48.183017image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T23:09:53.025238image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T23:09:57.915896image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T23:10:03.117215image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T23:10:07.373136image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T23:10:12.266029image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T23:10:17.437273image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T23:10:22.487130image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T23:10:27.547132image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T23:10:32.920187image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T23:10:36.734781image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T23:10:40.345566image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T23:10:45.681361image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T23:09:18.305387image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T23:09:23.186902image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T23:09:28.407940image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T23:09:33.339962image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T23:09:38.308101image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T23:09:43.408424image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T23:09:48.375977image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T23:09:53.212246image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T23:09:58.098369image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T23:10:03.302788image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T23:10:07.562727image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T23:10:12.457247image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T23:10:17.626279image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T23:10:22.678807image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T23:10:27.735546image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T23:10:33.121586image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T23:10:36.928452image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T23:10:40.574380image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T23:10:45.954681image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T23:09:18.564656image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T23:09:23.455621image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T23:09:28.659795image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T23:09:33.598012image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T23:09:38.588099image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T23:09:43.659075image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T23:09:48.631455image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T23:09:53.487165image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T23:09:58.376649image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T23:10:03.580730image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T23:10:07.790410image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T23:10:12.727352image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T23:10:17.884332image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T23:10:22.939610image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T23:10:28.011084image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T23:10:33.396906image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T23:10:37.119294image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-10T23:10:40.769549image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Missing values

2024-03-10T23:10:46.503034image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-10T23:10:48.354563image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Unnamed: 0Match IDDateVenueBat FirstBat SecondInningsOverBallBatterNon StrikerBowlerBatter RunsExtra RunsRuns From BallBall RebowledExtra TypeWicketMethodPlayer OutInnings RunsInnings WicketsTarget ScoreRuns to GetBalls RemainingWinnerChased SuccessfullyTotal Batter RunsTotal Non Striker RunsBatter Balls FacedNon Striker Balls FacedPlayer Out RunsPlayer Out Balls FacedBowler Runs ConcededValid Ball
0013396052023-03-26SuperSport ParkWest IndiesSouth Africa111BA KingKR MayersWD Parnell1010[]0NaNNaN10259NaN119South Africa11010NaNNaN11
1113396052023-03-26SuperSport ParkWest IndiesSouth Africa112KR MayersBA KingWD Parnell1010[]0NaNNaN20259NaN118South Africa11111NaNNaN11
2213396052023-03-26SuperSport ParkWest IndiesSouth Africa113BA KingKR MayersWD Parnell0000[]1caughtBA King21259NaN117South Africa101011.02.001
3313396052023-03-26SuperSport ParkWest IndiesSouth Africa114J CharlesKR MayersWD Parnell0000[]0NaNNaN21259NaN116South Africa10111NaNNaN01
4413396052023-03-26SuperSport ParkWest IndiesSouth Africa115J CharlesKR MayersWD Parnell4040[]0NaNNaN61259NaN115South Africa14121NaNNaN41
5513396052023-03-26SuperSport ParkWest IndiesSouth Africa116J CharlesKR MayersWD Parnell4040[]0NaNNaN101259NaN114South Africa18131NaNNaN41
6613396052023-03-26SuperSport ParkWest IndiesSouth Africa121KR MayersJ CharlesAK Markram0000[]0NaNNaN101259NaN113South Africa11823NaNNaN01
7713396052023-03-26SuperSport ParkWest IndiesSouth Africa122KR MayersJ CharlesAK Markram0000[]0NaNNaN101259NaN112South Africa11833NaNNaN01
8813396052023-03-26SuperSport ParkWest IndiesSouth Africa123KR MayersJ CharlesAK Markram0000[]0NaNNaN101259NaN111South Africa11843NaNNaN01
9913396052023-03-26SuperSport ParkWest IndiesSouth Africa124KR MayersJ CharlesAK Markram0000[]0NaNNaN101259NaN110South Africa11853NaNNaN01
Unnamed: 0Match IDDateVenueBat FirstBat SecondInningsOverBallBatterNon StrikerBowlerBatter RunsExtra RunsRuns From BallBall RebowledExtra TypeWicketMethodPlayer OutInnings RunsInnings WicketsTarget ScoreRuns to GetBalls RemainingWinnerChased SuccessfullyTotal Batter RunsTotal Non Striker RunsBatter Balls FacedNon Striker Balls FacedPlayer Out RunsPlayer Out Balls FacedBowler Runs ConcededValid Ball
42510942510911876672019-11-05Saxton OvalNew ZealandEngland2194S MahmoodTK CurranIS Sodhi1010[]0NaNNaN154718127.08New Zealand02424NaNNaN11
42511042511011876672019-11-05Saxton OvalNew ZealandEngland2195TK CurranS MahmoodIS Sodhi0111['wides']0NaNNaN155718126.08New Zealand04252NaNNaN10
42511142511111876672019-11-05Saxton OvalNew ZealandEngland2195TK CurranS MahmoodIS Sodhi6060[]0NaNNaN161718120.07New Zealand010262NaNNaN61
42511242511211876672019-11-05Saxton OvalNew ZealandEngland2196TK CurranS MahmoodIS Sodhi1010[]0NaNNaN162718119.06New Zealand011272NaNNaN11
42511342511311876672019-11-05Saxton OvalNew ZealandEngland2201TK CurranS MahmoodTG Southee0000[]0NaNNaN162718119.05New Zealand011282NaNNaN01
42511442511411876672019-11-05Saxton OvalNew ZealandEngland2202TK CurranS MahmoodTG Southee0000[]0NaNNaN162718119.04New Zealand011292NaNNaN01
42511542511511876672019-11-05Saxton OvalNew ZealandEngland2203TK CurranS MahmoodTG Southee1010[]0NaNNaN163718118.03New Zealand0122102NaNNaN11
42511642511611876672019-11-05Saxton OvalNew ZealandEngland2204S MahmoodTK CurranTG Southee0000[]0NaNNaN163718118.02New Zealand0212310NaNNaN01
42511742511711876672019-11-05Saxton OvalNew ZealandEngland2205S MahmoodTK CurranTG Southee1010[]0NaNNaN164718117.01New Zealand0312410NaNNaN11
42511842511811876672019-11-05Saxton OvalNew ZealandEngland2206TK CurranS MahmoodTG Southee2020[]0NaNNaN166718115.00New Zealand0143114NaNNaN21